Name
Abstract | ||
|---|---|---|
Identifying DNA splice sites is a main task of gene hunting. We introduce the hyper-network architecture as a novel method for finding DNA splice sites. The hypernetwork architecture is a biologically inspired information processing system composed of networks of molecules forming cells, and a number of cells forming a tissue or organism. Its learning is based on molecular evolution. DNA examples taken from GenBank were translated into binary strings and fed into a hypernetwork for training. We performed experiments to explore the generalization performance of hypernetwork learning in this data set by two-fold cross validation. The hypernetwork generalization performance was comparable to well known classification algorithms. With the best hypernetwork obtained, including local information and heuristic rules, we built a system (HyperExon) to obtain splice site candidates. The HyperExon system outperformed leading splice recognition systems in the list of sequences tested. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.biosystems.2006.09.004 | Biosystems |
Keywords | Field | DocType |
DNA splice sites identification,Artificial evolution,Hypernetwork learning,Molecular networks | Heuristic,Biology,Evolutionary algorithm,splice,Molecular evolution,Systems biology,Artificial intelligence,Statistical classification,Genetics,Cross-validation,GenBank,Machine learning | Journal |
Volume | Issue | ISSN |
87 | 2 | 0303-2647 |
Citations | PageRank | References |
2 | 0.40 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose L. Segovia-Juarez | 1 | 9 | 2.04 |
| Silvano Colombano | 2 | 72 | 10.37 |
| Denise E. Kirschner | 3 | 18 | 1.76 |